Method and system for key predictors and machine learning for configuring cell performance

ABSTRACT

A method for key predictors and machine learning for configuring battery cell performance may include providing a cell that includes a cathode, a separator, and a silicon-dominant anode; measuring a plurality of parameters of the cell; and using a machine learning model to determine cycle life based on the plurality of measured parameters, where one of the measured parameters includes second cycle coulombic efficiency. The plurality of parameters may include initial coulombic efficiency, cell impedance values, open-circuit voltage, cell thickness, and impedance after degassing. A first subset of the plurality of parameters may be measured before a formation process. A second subset of the plurality of parameters may be measured during a formation process, where the plurality of parameters may include a voltage reached during a first 10% of a first formation cycle. A third subset of the plurality of parameters may be measured during cycling of the cell.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

N/A

FIELD

Aspects of the present disclosure relate to energy generation andstorage. More specifically, certain embodiments of the disclosure relateto a method and system for key predictors and machine learning forconfiguring cell performance.

BACKGROUND

Conventional approaches for battery configuration may be costly,cumbersome, and/or inefficient—e.g., they may be complex and/or timeconsuming to implement, and may limit battery lifetime.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present disclosureas set forth in the remainder of the present application with referenceto the drawings.

BRIEF SUMMARY

A system and/or method for key predictors and machine learning forconfiguring cell performance, substantially as shown in and/or describedin connection with at least one of the figures, as set forth morecompletely in the claims.

These and other advantages, aspects and novel features of the presentdisclosure, as well as details of an illustrated embodiment thereof,will be more fully understood from the following description anddrawings.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a diagram of a battery, in accordance with an exampleembodiment of the disclosure.

FIG. 2A is a flow diagram of a lamination process for forming asilicon-dominant anode cell, in accordance with an example embodiment ofthe disclosure.

FIG. 2B is a flow diagram of a direct coating process for forming asilicon-dominant anode cell, in accordance with an example embodiment ofthe disclosure.

FIG. 3 illustrates a machine learning model for predicting batteryperformance, in accordance with an example embodiment of the disclosure.

FIGS. 4A-4D illustrates cycle performance predictions versus actualperformance for four different systems with different anode and cathodecombinations, in accordance with an example embodiment of thedisclosure.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a diagram of a battery with silicon-dominant anodes, inaccordance with an example embodiment of the disclosure. Referring toFIG. 1, there is shown a battery 100 comprising a separator 103sandwiched between an anode 101 and a cathode 105, with currentcollectors 107A and 107B. There is also shown a load 109 coupled to thebattery 100 illustrating instances when the battery 100 is in dischargemode. In this disclosure, the term “battery” may be used to indicate asingle electrochemical cell, a plurality of electrochemical cells formedinto a module, and/or a plurality of modules formed into a pack.Furthermore, the battery 100 shown in FIG. 1 is a very simplifiedexample merely to show the principle of operation of a lithium ion cell.Examples of realistic structures are shown to the right in FIG. 1, wherestacks of electrodes and separators are utilized, with electrodecoatings typically on both sides of the current collectors. The stacksmay be formed into different shapes, such as a coin cell, cylindricalcell, or prismatic cell, for example.

The development of portable electronic devices and electrification oftransportation drive the need for high performance electrochemicalenergy storage. Small-scale (<100 Wh) to large-scale (>10 KWh) devicesprimarily use lithium-ion (Li-ion) batteries over other rechargeablebattery chemistries due to their high-performance.

The anode 101 and cathode 105, along with the current collectors 107Aand 107B, may comprise the electrodes, which may comprise plates orfilms within, or containing, an electrolyte material, where the platesmay provide a physical barrier for containing the electrolyte as well asa conductive contact to external structures. In other embodiments, theanode/cathode plates are immersed in electrolyte while an outer casingprovides electrolyte containment. The anode 101 and cathode areelectrically coupled to the current collectors 107A and 1078, whichcomprise metal or other conductive material for providing electricalcontact to the electrodes as well as physical support for the activematerial in forming electrodes.

The configuration shown in FIG. 1 illustrates the battery 100 indischarge mode, whereas in a charging configuration, the load 109 may bereplaced with a charger to reverse the process. In one class ofbatteries, the separator 103 is generally a film material, made of anelectrically insulating polymer, for example, that prevents electronsfrom flowing from anode 101 to cathode 105, or vice versa, while beingporous enough to allow ions to pass through the separator 103.Typically, the separator 103, cathode 105, and anode 101 materials areindividually formed into sheets, films, or active material coated foils.Sheets of the cathode, separator and anode are subsequently stacked orrolled with the separator 103 separating the cathode 105 and anode 101to form the battery 100. In some embodiments, the separator 103 is asheet and generally utilizes winding methods and stacking in itsmanufacture. In these methods, the anodes, cathodes, and currentcollectors (e.g., electrodes) may comprise films.

In an example scenario, the battery 100 may comprise a solid, liquid, orgel electrolyte. The separator 103 preferably does not dissolve intypical battery electrolytes such as compositions that may comprise:Ethylene Carbonate (EC), Fluoroethylene Carbonate (FEC), PropyleneCarbonate (PC), Dimethyl Carbonate (DMC), Ethyl Methyl Carbonate (EMC),Diethyl Carbonate (DEC), etc. with dissolved LiBF₄, LiAsF₆, LiPF₆, andLiClO₄ etc. In an example scenario, the electrolyte may comprise Lithiumhexafluorophosphate (LiPF₆) and lithiumbis(trifluoromethanesulfonyl)imide (LiTFSI) that may be used together ina variety of electrolyte solvents. Lithium hexafluorophosphate (LiPF₆)may be present at a concentration of about 0.1 to 2.0 molar (M) andlithium bis(trifluoromethanesulfonyl)imide (LiTFSI) may be present at aconcentration of about 0 to 2.0 molar (M). Solvents may comprise one ormore of ethylene carbonate (EC), fluoroethylene carbonate (FEC) and/orethyl methyl carbonate (EMC) in various percentages. In someembodiments, the electrolyte solvents may comprise one or more of ECfrom about 0-40%, FEC from about 2-40% and/or EMC from about 50-70% byweight.

The separator 103 may be wet or soaked with a liquid or gel electrolyte.In addition, in an example embodiment, the separator 103 does not meltbelow about 100 to 120° C., and exhibits sufficient mechanicalproperties for battery applications. A battery, in operation, canexperience expansion and contraction of the anode and/or the cathode. Inan example embodiment, the separator 103 can expand and contract by atleast about 5 to 10% without failing, and may also be flexible.

The separator 103 may be sufficiently porous so that ions can passthrough the separator once wet with, for example, a liquid or gelelectrolyte. Alternatively (or additionally), the separator may absorbthe electrolyte through a gelling or other process even withoutsignificant porosity. The porosity of the separator 103 is alsogenerally not too porous to allow the anode 101 and cathode 105 totransfer electrons through the separator 103.

The anode 101 and cathode 105 comprise electrodes for the battery 100,providing electrical connections to the device for transfer ofelectrical charge in charge and discharge states. The anode 101 maycomprise silicon, carbon, or combinations of these materials, forexample. Typical anode electrodes comprise a carbon material thatincludes a current collector such as a copper sheet. Carbon is oftenused because it has excellent electrochemical properties and is alsoelectrically conductive. Anode electrodes currently used in rechargeablelithium-ion cells typically have a specific capacity of approximately200 milliamp hours per gram. Graphite, the active material used in mostlithium ion battery anodes, has a theoretical energy density of 372milliamp hours per gram (mAh/g). In comparison, silicon has a hightheoretical capacity of 4200 mAh/g. In order to increase volumetric andgravimetric energy density of lithium-ion batteries, silicon may be usedas the active material for the cathode or anode. Silicon anodes may beformed from silicon composites, with more than 50% silicon or more byweight in the anode material on the current collector, for example.

In an example scenario, the anode 101 and cathode 105 store the ion usedfor separation of charge, such as lithium. In this example, theelectrolyte carries positively charged lithium ions from the anode 101to the cathode 105 in discharge mode, as shown in FIG. 1 for example,and vice versa through the separator 105 in charge mode. The movement ofthe lithium ions creates free electrons in the anode 101 which creates acharge at the positive current collector 1078. The electrical currentthen flows from the current collector through the load 109 to thenegative current collector 107A. The separator 103 blocks the flow ofelectrons inside the battery 100, allows the flow of lithium ions, andprevents direct contact between the electrodes.

While the battery 100 is discharging and providing an electric current,the anode 101 releases lithium ions to the cathode 105 via the separator103, generating a flow of electrons from one side to the other via thecoupled load 109. When the battery is being charged, the oppositehappens where lithium ions are released by the cathode 105 and receivedby the anode 101.

The materials selected for the anode 101 and cathode 105 are importantfor the reliability and energy density possible for the battery 100. Theenergy, power, cost, and safety of current Li-ion batteries need to beimproved in order to, for example, compete with internal combustionengine (ICE) technology and allow for the widespread adoption ofelectric vehicles (EVs). High energy density, high power density, andimproved safety of lithium-ion batteries are achieved with thedevelopment of high-capacity and high-voltage cathodes, high-capacityanodes and functionally non-flammable electrolytes with high voltagestability and interfacial compatibility with electrodes. In addition,materials with low toxicity are beneficial as battery materials toreduce process cost and promote consumer safety.

The performance of electrochemical electrodes, while dependent on manyfactors, is largely dependent on the robustness of electrical contactbetween electrode particles, as well as between the current collectorand the electrode particles. The electrical conductivity of siliconanode electrodes may be manipulated by incorporating conductiveadditives with different morphological properties. Carbon black(SuperP), vapor grown carbon fibers (VGCF), and a mixture of the twohave previously been incorporated separately into the anode electroderesulting in improved performance of the anode. The synergisticinteractions between the two carbon materials may facilitate electricalcontact throughout the large volume changes of the silicon anode duringcharge and discharge.

State-of-the-art lithium-ion batteries typically employ agraphite-dominant anode as an intercalation material for lithium.Silicon-dominant anodes, however, offer improvements compared tographite-dominant Li-ion batteries. Silicon exhibits both highergravimetric (4200 mAh/g vs. 372 mAh/g for graphite) and volumetriccapacities (2194 mAh/L vs. 890 mAh/L for graphite). In addition,silicon-based anodes have a low lithiation/delithiation voltage plateauat about 0.3-0.4V vs. Li/Li+, which allows it to maintain an opencircuit potential that avoids undesirable Li plating and dendriteformation. While silicon shows excellent electrochemical activity,achieving a stable cycle life for silicon-based anodes is challengingdue to silicon's large volume changes during lithiation anddelithiation. Silicon regions may lose electrical contact from the anodeas large volume changes coupled with its low electrical conductivityseparate the silicon from surrounding materials in the anode.

In addition, the large silicon volume changes exacerbate solidelectrolyte interphase (SEI) formation, which can further lead toelectrical isolation and, thus, capacity loss. Expansion and shrinkageof silicon particles upon charge-discharge cycling causes pulverizationof silicon particles, which increases their specific surface area. Asthe silicon surface area changes and increases during cycling, SEIrepeatedly breaks apart and reforms. The SEI thus continually builds uparound the pulverizing silicon regions during cycling into a thickelectronic and ionic insulating layer. This accumulating SEI increasesthe impedance of the electrode and reduces the electrode electrochemicalreactivity, which is detrimental to cycle life.

Cell cycle life is an important parameter, no matter the application ofthe cell. Multiple variables may impact the cell life, and it may bedifficult to determine at the time of fabrication of a particular cell,how long it will last. Various parameters that may be correlated to celllife comprise early cell impedance, open circuit voltage, thickness,initial coulombic efficiency (discharge capacity/charge capacity),formation cycle charge capacity, formation cycle discharge capacity,difference between voltage curves at particular cycle numbers, andmaximum voltage reached during the first 10% of the first formationcharge cycle, for example.

In an example embodiment, data may be taken from a plurality of cellsand using machine learning, a model may be generated that may beutilized to predict cell performance. In this manner, cells may bebinned in different performance level categories when manufactured withconsistent performance for cells selected from the same bin, withoutwaiting for weeks or months for cycle data.

In this disclosure, a wide number of parameters are utilized in machinelearning models to predict performance, with five categories ofinputs: 1) measurements of the cells or cell components before formationor cycling such as impedance values, open circuit voltage values, cellthickness; 2) features taken/measured from the formation cycles such asvoltage values, and coulombic efficiency during formation; 3) data takenfrom the first ten cycles, including discharge capacity, chargecapacity, coulombic efficiency, resistance values (one data point percycle); 4) statistical measurements comparing two different voltagecurves within the first 10 cycles; and 5) measurements ofcharacteristics of the cell components prior to cell assembly includingcharacteristics of the raw materials used to fabricate the cells,physical properties of the electrolyte, or fundamental characteristicsof the electrodes such as resistance, roughness, or mechanical strength.

FIG. 2A is a flow diagram of a lamination process for forming asilicon-dominant anode cell, in accordance with an example embodiment ofthe disclosure. This process employs a high-temperature pyrolysisprocess on a substrate, layer removal, and a lamination process toadhere the active material layer to a current collector.

The raw electrode active material is mixed in step 201. In the mixingprocess, the active material may be mixed, e.g., a binder/resin (such asPI, PAI), solvent, and conductive additives. The materials may comprisecarbon nanotubes/fibers, graphene sheets, metal polymers, metals,semiconductors, and/or metal oxides, for example. Silicon powder with a1-30 or 5-30 μm particle size, for example, may then be dispersed inpolyamic acid resin (15% solids in N-Methyl pyrrolidone (NMP)) at, e.g.,1000 rpm for, e.g., 10 minutes, and then the conjugated carbon/NMPslurry may be added and dispersed at, e.g., 2000 rpm for, e.g., 10minutes to achieve a slurry viscosity within 2000-4000 cP and a totalsolid content of about 30%.

In step 203, the slurry may be coated on a substrate. In this step, theslurry may be coated onto a Polyester, polyethylene terephthalate (PET),or Mylar film at a loading of, e.g., 2-4 mg/cm² and then undergo dryingto an anode coupon with high Si content and less than 15% residualsolvent content. This may be followed by an optional calendering processin step 205, where a series of hard pressure rollers may be used tofinish the film/substrate into a smoothed and denser sheet of material.

In step 207, the green film may then be removed from the PET, where theactive material may be peeled off the polymer substrate, the peelingprocess being optional for a polypropylene (PP) substrate, since PP canleave ˜2% char residue upon pyrolysis. The peeling may be followed by apyrolysis step 209 where the material may be heated to 600-1250 C for1-3 hours, cut into sheets, and vacuum dried using a two-stage process(120° C. for 15 h, 220° C. for 5 h).

In step 211, the electrode material may be laminated on a currentcollector. For example, a 5-20 μm thick copper foil may be coated withpolyamide-imide with a nominal loading of, e.g., 0.2-0.6 mg/cm² (appliedas a 6 wt % varnish in NMP and dried for, e.g., 12-18 hours at, e.g.,110° C. under vacuum). The anode coupon may then be laminated on thisadhesive-coated current collector. In an example scenario, thesilicon-carbon composite film is laminated to the coated copper using aheated hydraulic press. An example lamination press process comprises30-70 seconds at 300° C. and 3000-5000 psi, thereby forming the finishedsilicon-composite electrode.

In step 213, the cell may be assessed before being subject to aformation process. The measurements may comprise impedance values, opencircuit voltage, and thickness measurements. During formation, theinitial lithiation of the anode may be performed, followed bydelithiation. Cells may be clamped during formation and/or earlycycling. The formation cycles are defined as any type ofcharge/discharge of the cell that is performed to prepare the cell forgeneral cycling and is considered part of the cell production process.Different rates of charge and discharge may be utilized in formationsteps. During formation, various cell measurements may comprise initialcoulombic efficiency, which is the discharge capacity divided by thecharge capacity, the formation cycle charge capacity, and formationcycle discharge capacity. Other measurements comprise voltage reachedduring the first 10% of the first formation cycle and comparisonsbetween cell voltage curves during any portion of the formation cycle.Data collected on the cell before and during formation may be utilizedwith a machine learning model to predict the cell's performance duringits lifetime.

FIG. 2B is a flow diagram of a direct coating process for forming asilicon-dominant anode cell, in accordance with an example embodiment ofthe disclosure. This process comprises physically mixing the activematerial, conductive additive, and binder together, and coating itdirectly on a current collector before pyrolysis. This example processcomprises a direct coating process in which an anode or cathode slurryis directly coated on a copper foil using a binder such as CMC, SBR,Sodium Alginate, PAI, PI and mixtures and combinations thereof.

In step 221, the active material may be mixed, e.g., a binder/resin(such as PI, PAI), solvent, and conductive additives. The materials maycomprise carbon nanotubes/fibers, graphene sheets, metal polymers,metals, semiconductors, and/or metal oxides, for example. Silicon powderwith a 5-30 μm particle size, for example, may then be dispersed inpolyamic acid resin (15% solids in N-Methyl pyrrolidone (NMP)) at, e.g.,1000 rpm for, e.g., 10 minutes, and then the conjugated carbon/NMPslurry may be added and dispersed at, e.g., 2000 rpm for, e.g., 10minutes to achieve a slurry viscosity within 2000-4000 cP and a totalsolid content of about 30%.

Furthermore, cathode active materials may be mixed in step 221, wherethe active material may comprise lithium cobalt oxide (LCO), lithiumiron phosphate, lithium nickel cobalt manganese oxide (NMC), lithiumnickel cobalt aluminum oxide (NCA), lithium manganese oxide (LMO),lithium nickel manganese spinel, or similar materials or combinationsthereof, mixed with a binder as described above for the anode activematerial.

In step 223, the slurry may be coated on a copper foil. In the directcoating process described here, an anode slurry is coated on a currentcollector with residual solvent followed by a calendaring process fordensification followed by pyrolysis (˜500-800 C) such that carbonprecursors are partially or completely converted into glassy carbon.Similarly, cathode active materials may be coated on a foil material,such as aluminum, for example. The active material layer may undergo adrying in step 225 resulting in reduced residual solvent content. Anoptional calendering process may be utilized in step 227 where a seriesof hard pressure rollers may be used to finish the film/substrate into asmoother and denser sheet of material. In step 227, the foil and coatingproceeds through a roll press for lamination.

In step 229, the active material may be pyrolyzed by heating to500-1000° C. such that carbon precursors are partially or completelyconverted into glassy carbon. Pyrolysis can be done either in roll formor after punching. If done in roll form, the punching is done after thepyrolysis process. The pyrolysis step may result in an anode activematerial having silicon content greater than or equal to 50% by weight,where the anode has been subjected to heating at or above 400 degreesCelsius. In an example scenario, the anode active material layer maycomprise 20 to 95% silicon and in yet another example scenario maycomprise 50 to 95% silicon by weight. In instances where the currentcollector foil is not pre-punched/pre-perforated, the formed electrodemay be perforated with a punching roller, for example. The punchedelectrodes may then be sandwiched with a separator and electrolyte toform a cell.

In step 233, the cell may be assessed before being subject to aformation process. The measurements may comprise impedance values, opencircuit voltage, and thickness measurements. During formation, theinitial lithiation of the anode may be performed, followed bydelithiation. Cells may be clamped during formation and/or earlycycling. The formation cycles are defined as any type ofcharge/discharge of the cell that is performed to prepare the cell forgeneral cycling and is considered part of the cell production process.Different rates of charge and discharge may be utilized in formationsteps. During formation, various cell measurements may be made with dataanalyzed using a machine learning model to predict the cell'sperformance during its lifetime.

FIG. 3 illustrates a machine learning model for predicting batteryperformance, in accordance with an example embodiment of the disclosure.Referring to FIG. 3, there is shown machine learning process 300comprising data 301 measured from a single cell, a cell database 303,and resulting models 305. The types of data in the data 301 may comprisethe cell design (type of anode/cathode, separator, electrolyte, etc.),quality measurements, cycle performance, and electrical data. The amountof these types of data range from tens for cell design and qualitymeasurements, to the thousands for cycle performance, to millions forelectrical data. The measurements may comprise largely electricalmeasurements, made with an apparatus with multimeter capability fortaking current, voltage, and impedance measurements. Other examplemeasurements may comprise physical measurements, such asthickness/volume, temperature, and pressure. The data may includeinformation about the cell components prior to cell assembly, such asphysical properties of the raw materials or of the individual electrodesor electrolyte

The machine learning model 305 comprises a cell performance predictorwith one or multiple input data features, and may be trained usingMachine Learning (ML) algorithms. The model can perform eitherregression (prediction of a continuous value) or classification(prediction of a categorical value). The performance metrics include,but are not limited to: number of cycles above a capacity threshold,total cycled capacity, total cycled energy, total cycled energy density,number of cycles below a resistance threshold, and number of cyclesbelow a thickness threshold.

The machine learning algorithms used to train the model include, but arenot limited to: linear regression, logistic regression, lassoregression, AdaBoost regression, AdaBoost classification, XGBoostregression, XGBoost classification, Random Forest regression, Randomforest classification, Multi-layer perception, long-short-term-memoryneural networks, and Bayesian networks. Models that are trained usingmachine learning algorithms other than linear or logistic regressionachieve better performance than linear or logistic regression, withperformances defined by any of the following: R-squared value,root-mean-squared error, average error, receiver operatingcharacteristic analysis (for classification).

The model input may, but does not have to, comprise only data acquiredbefore the cell has undergone any cycling. In some cases, the model maypredict the performance of cells that have different cell design(different combinations of anode, cathode, separator, electrolyte, cellsize, number of electrode layers, cell form factor) than the cells inthe data used to train the model. In an example embodiment, the data maycomprise may any characteristic or measurement of the individualcomponents of the cells prior to cell assembly, or of the raw materialsused to fabricate those components. For example, for electrodes, thedata may comprise loading, conductivity, mechanical strength,wettability, pyrolysis temperature, resin formulation, active materialtype, and active material particle size. For electrolytes, the data maycomprise conductivity, viscosity, and formulation, whereas forseparators the data may comprise porosity and tortuosity, for example.

The following data features may be used as input in any combination bythe machine learning model. They generally fall into five categories,but the placement of features into these categories does not affect howthey are used in the model. Category 1) comprises cell measurements NOTacquired during cycling, which are measurements of cell properties thatare not collected while current is flowing through the cell, and maycomprise: 1) Impedance, 2) open circuit voltage, and 3) thickness. Thesemeasurements may be acquired multiple times during the preparation ofthe cell; each measurement occurrence may comprise a unique feature.Measurement occurrences may comprise, but are not limited to: afterclamping, after degassing, after formation, and unclamped beforedegassing. Degassing occurs when a cell generates gas products duringformation and/or initial cycles, with the gas removed from the cell.

In category 2), the machine learning model 305 may utilize cellmeasurements acquired during formation. These are measurements takenduring the formation cycle(s) of the cell but not during the subsequentcycling. The formation cycles may be defined as any type ofcharge/discharge of the cell that is performed to prepare the cell forgeneral cycling and is considered part of the cell production process.These measurements may comprise initial coulombic efficiency, which isthe discharge capacity divided by the charge capacity, the formationcycle charge capacity, and formation cycle discharge capacity. Othermeasurements comprise voltage reached during the first 10% of the firstformation cycle and comparisons between cell voltage curves during anyportion of the formation cycle.

Statistical calculations may also be performed on the difference betweenthe cell voltage curve during any portion of the formation cycle(s) andthe voltage curve of a reference cell's formation cycle. The voltagecurve may be defined as the voltage vs. capacity data points that areacquired as the cell is cycled. The cell voltage curve and referencevoltage curve may be interpolated to have either identical capacityvalues or identical voltage values. The curves may then be then comparedat each of these values. The following calculations on the comparisonbetween the cell and reference curve may be used: maximum cell minusreference, maximum reference minus cell, mean cell minus reference,mean-squared cell minus reference, and variance of cell minus reference.

Category 3) may comprise discrete cycling features. These are datapoints that may be collected on a per-cycle basis, measured during thecharge/discharge of the cell. The features may be collected from anycycle number and from multiple cycle numbers. Each cycle number/datatype combination comprises a unique feature. Typically, features may beextracted from cycle numbers ten and below, but there is no limitationof the cycle number(s) used. Parameters may comprise discharge capacity,charge capacity, coulombic efficiency, resistance, duration of aconstant-current portion of a cycle, duration of a constant-voltageportion of a cycle, midpoint voltage (the voltage at the 50% capacitypoint of a cycle), voltage after a rest (zero current) portion of acycle, capacity at a specific voltage, and temperature at a specificvoltage or capacity.

Category 4) may comprise statistical calculations performed on thedifference between voltage curves from two different cycle numbers isyet another example parameter. The voltage curve is defined as thevoltage vs. capacity data points that are acquired as the cell iscycled. The two voltage curves may be interpolated to have eitheridentical capacity values or identical voltage values. The curves maythen be compared at each of these values. Typically, features may beextracted from cycle numbers ten and below, but there is no limitationof the cycle number(s) used.

Category 5) may comprise any characteristic or measurement of theindividual components of the cells prior to cell assembly, or of the rawmaterials used to fabricate those components. The range of possible datain this category is broad but some examples include: for electrodes:loading, conductivity, mechanical strength, wettability, pyrolysistemperature, resin formulation, active material type, active materialparticle size; for electrolytes: conductivity, viscosity, formulation;for separators: porosity, tortuosity.

In one example “cycle x” is denoted as the lower cycle number and “cycley” as the higher cycle number. Example parameters may comprise maximumcycle x minus cycle y, maximum negative cycle x minus cycle y, meancycle x minus cycle y, variance cycle x minus cycle y, Log variancecycle x minus cycle y, reciprocal of cycle x minus cycle y.

In certain cases, good results may be achieved using only data fromcategories 1) and 5), parameters measured when not cycling, and in aslightly larger number of cases, only data from categories 1), 2), and5) also including measurements during formation. One of the mainadvantages of the machine learning model described here is that it worksacross different cell designs and predicts the performance of the cellbefore it has undergone any operation.

The models 305 illustrate results for a linear regression model, shownby the “X” points on the plot and the machine learning model, shown bythe circles, where the y-axis is the predicted performance and thex-axis is the actual result. The machine learning model shows muchbetter prediction capability with a root-mean-square error of 14compared to the linear regression root-mean-square error of 57.

FIGS. 4A-4D illustrates cycle performance predictions versus actualperformance for four different systems with different anode and cathodecombinations, in accordance with an example embodiment of thedisclosure. Here the data shows across four different systems, withdifferent anode cathode combinations, when the cells were cycled atdifferent depths of charge and/or discharge state, where the cycle lifeto 80% capacity retention is well correlated with the coulombicefficiency in the second charge/discharge cycle. This model predictscycle life based on the result of just a few cycles, and potentiallysaves time, money and resources, as well as increaseresearch/development speed. The second cycle measurement may beperformed during formation and/or regular cycling.

FIG. 4A illustrates data for a cell with a bonded, or laminated anode,as described with respect to FIG. 2A, with an NCA cathode. The plotshows that the coulombic efficiency of the 2^(nd) cycle can reasonablypredict cycle life with an R² of 0.9666. FIG. 4B shows data for a bondedanode with NCM811 cathode system. In this case, the coulombic efficiencyof the 2^(nd) cycle can reasonably predict cycle life with an R² of0.9597.

FIG. 4C illustrates data for a cell with a continuous anode, asdescribed with respect to FIG. 2B, with an NCA cathode. In this example,the coulombic efficiency of the 2^(nd) cycle can reasonably predictcycle life with R² of 0.8341 (and an R² of 0.9983 not including oneoutlier). Finally, FIG. 4D also illustrates data for a continuous anodebut with an NCM811 cathode. With this system, the coulombic efficiencyof the 2^(nd) cycle can reasonably predict cycle life with an R² of0.944.

Each of these plots indicate that a machine learning model consideringthe 2^(nd) cycle coulombic efficiency can accurately predict cellperformance, as measured by cycle life, in this case indicated by thenumber of cycles to reach 80% of the initial capacity.

In an example embodiment of the disclosure, a method and system isdescribed for key predictors and machine learning for configuring cellperformance, and may include providing a cell comprising a cathode, aseparator, and a silicon-dominant anode; measuring a plurality ofparameters of the cell; and using a machine learning model to determinecycle life based on the plurality of measured parameters, where one ofthe measured parameters comprises second cycle coulombic efficiency,which may be measured during formation and/or cell cycling. Theplurality of parameters may include initial coulombic efficiency, cellimpedance values, open-circuit voltage, cell thickness, and impedanceafter degassing.

The plurality of measured parameters may comprise second cycle coulombicefficiency. The plurality of parameters comprise characteristics of cellcomponents or raw materials prior to assembly. The data prior toassembly may comprise, for electrodes, one or more of: loading,conductivity, mechanical strength, wettability, pyrolysis temperature,resin formulation, active material type, and active material particlesize. The data prior to assembly may comprise, for electrolytes, one ormore of: conductivity, viscosity, and formulation. The data prior toassembly may comprise, for separators: porosity and/or tortuosity.

A first subset of the plurality of parameters may be measured before aformation process. A second subset of the plurality of parameters may bemeasured during a formation process, where the plurality of parametersmay comprise a voltage reached during a first 10% of a first formationcycle. A third subset of the plurality of parameters may be measuredduring cycling of the cell, where the plurality of parameters maycomprise a comparison of two different voltage curves of the cell withina first ten cycles, and where the voltage curves comprise voltage vs.capacity data points that are acquired as the cell is cycled.

The plurality of parameters may comprise a charge capacity of the cellwithin a first ten cycles and/or a discharge capacity of the cell withina first ten cycles. The cycle life may be defined as a number of cyclesto reach 80% of initial capacity. The machine learning model may utilizeone or more of the following: logistic regression, lasso regression,AdaBoost regression, AdaBoost classification, XGBoost regression,XGBoost classification, random forest regression, random forestclassification, multi-layer perception, long-short-term-memory neuralnetworks, and Bayesian networks. In terms of methodologies, decisiontree, linear, neural network, or deep learning can be used. A preferredmethod may comprise a gradient boosted decision tree algorithm.

As utilized herein the terms “circuits” and “circuitry” refer tophysical electronic components (i.e. hardware) and any software and/orfirmware (“code”) which may configure the hardware, be executed by thehardware, and or otherwise be associated with the hardware. As usedherein, for example, a particular processor and memory may comprise afirst “circuit” when executing a first one or more lines of code and maycomprise a second “circuit” when executing a second one or more lines ofcode. As utilized herein, “and/or” means any one or more of the items inthe list joined by “and/or”. As an example, “x and/or y” means anyelement of the three-element set {(x), (y), (x, y)}. In other words, “xand/or y” means “one or both of x and y”. As another example, “x, y,and/or z” means any element of the seven-element set {(x), (y), (z), (x,y), (x, z), (y, z), (x, y, z)}. In other words, “x, y and/or z” means“one or more of x, y and z”. As utilized herein, the term “exemplary”means serving as a non-limiting example, instance, or illustration. Asutilized herein, the terms “e.g.,” and “for example” set off lists ofone or more non-limiting examples, instances, or illustrations. Asutilized herein, a battery, circuitry or a device is “operable” toperform a function whenever the battery, circuitry or device comprisesthe necessary hardware and code (if any is necessary) or other elementsto perform the function, regardless of whether performance of thefunction is disabled or not enabled (e.g., by a user-configurablesetting, factory trim, configuration, etc.).

While the present invention has been described with reference to certainembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the present invention. In addition, manymodifications may be made to adapt a particular situation or material tothe teachings of the present invention without departing from its scope.Therefore, it is intended that the present invention not be limited tothe particular embodiment disclosed, but that the present invention willinclude all embodiments falling within the scope of the appended claims.

The invention claimed is:
 1. A method of predicting battery performance,the method comprising: providing a cell comprising a cathode, aseparator, and a silicon-dominant anode; measuring, via a measurementapparatus configured for obtaining particular types of measurementsrelating to cells and operations of cells, a plurality of parameters ofthe cell; and using a machine learning model to predetermine cycle lifebased on the plurality of measured parameters; wherein the plurality ofparameters comprise characteristics of cell components or raw materialsprior to assembly; and wherein the data prior to assembly comprises, forelectrodes, one or more of: loading, conductivity, mechanical strength,wettability, pyrolysis temperature, resin formulation, active materialtype, and active material particle size.
 2. The method of claim 1,wherein the plurality of parameters comprise initial coulombicefficiency.
 3. The method of claim 1, wherein the plurality of measuredparameters comprises second cycle coulombic efficiency.
 4. The method ofclaim 1, wherein the plurality of parameters comprise cell impedancevalues.
 5. The method of claim 1, wherein the plurality of parameterscomprise open-circuit voltage.
 6. The method of claim 1, wherein theplurality of parameters comprise cell thickness.
 7. The method of claim1, wherein the plurality of parameters comprise impedance afterdegassing.
 8. The method of claim 1, comprising measuring a first subsetof the plurality of parameters before a formation process.
 9. The methodof claim 1, comprising measuring a second subset of the plurality ofparameters during a formation process.
 10. The method according to claim9, wherein the plurality of parameters comprise a voltage reached duringa first 10% of a first formation cycle.
 11. The method of claim 1,comprising measuring a third subset of the plurality of parametersduring cycling of the cell.
 12. The method of claim 11, wherein theplurality of parameters comprise a comparison of two different voltagecurves of the cell within a first ten cycles, wherein the voltage curvescomprise voltage vs. capacity data points that are acquired as the cellis cycled.
 13. The method of claim 11, wherein the plurality ofparameters comprise a charge capacity of the cell within a first tencycles.
 14. The method of claim 11, wherein the plurality of parameterscomprise a discharge capacity of the cell within a first ten cycles. 15.The method according to claim 1, wherein the cycle life is defined as anumber of cycles to reach 60-80% of initial capacity.
 16. A method ofpredicting battery performance, the method comprising: providing a cellcomprising a cathode, a separator, and a silicon-dominant anode;measuring, via a measurement apparatus configured for obtainingparticular types of measurements relating to cells and operations ofcells, a plurality of parameters of the cell; and using a machinelearning model to predetermine cycle life based on the plurality ofmeasured parameters; wherein the plurality of parameters comprisecharacteristics of cell components or raw materials prior to assembly;and wherein the data prior to assembly comprises, for electrolytes, oneor more of: conductivity, viscosity, and formulation.
 17. The method ofclaim 16, wherein the plurality of parameters comprise initial coulombicefficiency.
 18. The method of claim 16, wherein the plurality ofmeasured parameters comprises second cycle coulombic efficiency.
 19. Themethod of claim 16, wherein the plurality of parameters comprise cellimpedance values.
 20. The method of claim 16, wherein the plurality ofparameters comprise open-circuit voltage.
 21. The method of claim 16,wherein the plurality of parameters comprise cell thickness.
 22. Themethod of claim 16, wherein the plurality of parameters compriseimpedance after degassing.
 23. The method of claim 16, comprisingmeasuring a first subset of the plurality of parameters before aformation process.
 24. The method of claim 16, comprising measuring asecond subset of the plurality of parameters during a formation process.25. The method according to claim 24, wherein the plurality ofparameters comprise a voltage reached during a first 10% of a firstformation cycle.
 26. The method of claim 16, comprising measuring athird subset of the plurality of parameters during cycling of the cell.27. The method of claim 26, wherein the plurality of parameters comprisea comparison of two different voltage curves of the cell within a firstten cycles, wherein the voltage curves comprise voltage vs. capacitydata points that are acquired as the cell is cycled.
 28. The method ofclaim 26, wherein the plurality of parameters comprise a charge capacityof the cell within a first ten cycles.
 29. The method of claim 26,wherein the plurality of parameters comprise a discharge capacity of thecell within a first ten cycles.
 30. The method according to claim 16,wherein the cycle life is defined as a number of cycles to reach 60-80%of initial capacity.
 31. A method of predicting battery performance, themethod comprising: providing a cell comprising a cathode, a separator,and a silicon-dominant anode; measuring, via a measurement apparatusconfigured for obtaining particular types of measurements relating tocells and operations of cells, a plurality of parameters of the cell;and using a machine learning model to predetermine cycle life based onthe plurality of measured parameters; wherein the plurality ofparameters comprise characteristics of cell components or raw materialsprior to assembly; and wherein the data prior to assembly comprises, forseparators: porosity and/or tortuosity.
 32. The method of claim 31,wherein the plurality of parameters comprise initial coulombicefficiency.
 33. The method of claim 31, wherein the plurality ofmeasured parameters comprises second cycle coulombic efficiency.
 34. Themethod of claim 31, wherein the plurality of parameters comprise cellimpedance values.
 35. The method of claim 31, wherein the plurality ofparameters comprise open-circuit voltage.
 36. The method of claim 31,wherein the plurality of parameters comprise cell thickness.
 37. Themethod of claim 31, wherein the plurality of parameters compriseimpedance after degassing.
 38. The method of claim 31, comprisingmeasuring a first subset of the plurality of parameters before aformation process.
 39. The method of claim 31, comprising measuring asecond subset of the plurality of parameters during a formation process.40. The method according to claim 39, wherein the plurality ofparameters comprise a voltage reached during a first 10% of a firstformation cycle.
 41. The method of claim 31, comprising measuring athird subset of the plurality of parameters during cycling of the cell.42. The method of claim 41, wherein the plurality of parameters comprisea comparison of two different voltage curves of the cell within a firstten cycles, wherein the voltage curves comprise voltage vs. capacitydata points that are acquired as the cell is cycled.
 43. The method ofclaim 41, wherein the plurality of parameters comprise a charge capacityof the cell within a first ten cycles.
 44. The method of claim 41,wherein the plurality of parameters comprise a discharge capacity of thecell within a first ten cycles.
 45. The method according to claim 31,wherein the cycle life is defined as a number of cycles to reach 60-80%of initial capacity.
 46. A method of predicting battery performance, themethod comprising: providing a cell comprising a cathode, a separator,and a silicon-dominant anode; measuring, via a measurement apparatusconfigured for obtaining particular types of measurements relating tocells and operations of cells, a plurality of parameters of the cell;using a machine learning model to predetermine cycle life based on theplurality of measured parameters; and measuring a third subset of theplurality of parameters during cycling of the cell; wherein theplurality of parameters comprise a comparison of two different voltagecurves of the cell within a first ten cycles, wherein the voltage curvescomprise voltage vs. capacity data points that are acquired as the cellis cycled.
 47. The method of claim 46, wherein the plurality ofparameters comprise initial coulombic efficiency.
 48. The method ofclaim 46, wherein the plurality of measured parameters comprises secondcycle coulombic efficiency.
 49. The method of claim 46, wherein theplurality of parameters comprise cell impedance values.
 50. The methodof claim 46, wherein the plurality of parameters comprise open-circuitvoltage.
 51. The method of claim 46, wherein the plurality of parameterscomprise cell thickness.
 52. The method of claim 46, wherein theplurality of parameters comprise impedance after degassing.
 53. Themethod of claim 46, comprising measuring a first subset of the pluralityof parameters before a formation process.
 54. The method of claim 46,comprising measuring a second subset of the plurality of parametersduring a formation process.
 55. The method according to claim 46,wherein the plurality of parameters comprise a voltage reached during afirst 10% of a first formation cycle.
 56. The method according to claim46, wherein the cycle life is defined as a number of cycles to reach60-80% of initial capacity.
 57. A method of predicting batteryperformance, the method comprising: providing a cell comprising acathode, a separator, and a silicon-dominant anode; measuring, via ameasurement apparatus configured for obtaining particular types ofmeasurements relating to cells and operations of cells, a plurality ofparameters of the cell; using a machine learning model to predeterminecycle life based on the plurality of measured parameters; and measuringa third subset of the plurality of parameters during cycling of thecell; wherein the plurality of parameters comprise a charge capacity ofthe cell within a first ten cycles.
 58. The method of claim 57, whereinthe plurality of parameters comprise initial coulombic efficiency. 59.The method of claim 57, wherein the plurality of measured parameterscomprises second cycle coulombic efficiency.
 60. The method of claim 57,wherein the plurality of parameters comprise cell impedance values. 61.The method of claim 57, wherein the plurality of parameters compriseopen-circuit voltage.
 62. The method of claim 57, wherein the pluralityof parameters comprise cell thickness.
 63. The method of claim 57,wherein the plurality of parameters comprise impedance after degassing.64. The method of claim 57, comprising measuring a first subset of theplurality of parameters before a formation process.
 65. The method ofclaim 57, comprising measuring a second subset of the plurality ofparameters during a formation process.
 66. The method according to claim65, wherein the plurality of parameters comprise a voltage reachedduring a first 10% of a first formation cycle.
 67. The method accordingto claim 57, wherein the cycle life is defined as a number of cycles toreach 60-80% of initial capacity.
 68. A method of predicting batteryperformance, the method comprising: providing a cell comprising acathode, a separator, and a silicon-dominant anode; measuring, via ameasurement apparatus configured for obtaining particular types ofmeasurements relating to cells and operations of cells, a plurality ofparameters of the cell; using a machine learning model to predeterminecycle life based on the plurality of measured parameters; and measuringa third subset of the plurality of parameters during cycling of thecell; wherein the plurality of parameters comprise a discharge capacityof the cell within a first ten cycles.
 69. The method of claim 68,wherein the plurality of parameters comprise initial coulombicefficiency.
 70. The method of claim 68, wherein the plurality ofmeasured parameters comprises second cycle coulombic efficiency.
 71. Themethod of claim 68, wherein the plurality of parameters comprise cellimpedance values.
 72. The method of claim 68, wherein the plurality ofparameters comprise open-circuit voltage.
 73. The method of claim 68,wherein the plurality of parameters comprise cell thickness.
 74. Themethod of claim 68, wherein the plurality of parameters compriseimpedance after degassing.
 75. The method of claim 68, comprisingmeasuring a first subset of the plurality of parameters before aformation process.
 76. The method of claim 68, comprising measuring asecond subset of the plurality of parameters during a formation process.77. The method according to claim 76, wherein the plurality ofparameters comprise a voltage reached during a first 10% of a firstformation cycle.
 78. The method according to claim 68, wherein the cyclelife is defined as a number of cycles to reach 60-80% of initialcapacity.